The Bi-objective Long-haul Transportation Problem on a Road Network

TL;DR

This paper introduces a bi-objective optimization algorithm for long-haul truck routing that minimizes fuel costs and travel time while considering refueling, regulations, and heterogeneous fuel prices, outperforming current practices.

Contribution

It presents a novel interactive algorithm that constructs a set of non-dominated paths for long-haul trucks considering multiple real-world constraints.

Findings

Algorithm produces significantly lower fuel costs than current practices.

Generated solutions offer a manageable number of alternatives for decision makers.

Method effectively handles large-scale road networks with real-world data.

Abstract

In this paper we study a long-haul truck scheduling problem where a path has to be determined for a vehicle traveling from a specified origin to a specified destination. We consider refueling decisions along the path, while accounting for heterogeneous fuel prices in a road network. Furthermore, the path has to comply with Hours of Service (HoS) regulations. Therefore, a path is defined by the actual road trajectory traveled by the vehicle, as well as the locations where the vehicle stops due to refueling, compliance with HoS regulations, or a combination of the two. This setting is cast in a bi-objective optimization problem, considering the minimization of fuel cost and the minimization of path duration. An algorithm is proposed to solve the problem on a road network. The algorithm builds a set of non-dominated paths with respect to the two objectives. Given the enormous theoretical size of the road network, the algorithm follows an interactive path construction mechanism. Specifically, the algorithm dynamically interacts with a geographic information system to identify the relevant potential paths and stop locations. Computational tests are made on real-sized instances where the distance covered ranges from 500 to 1500 km. The algorithm is compared with solutions obtained from a policy mimicking the current practice of a logistics company. The results show that the non-dominated solutions produced by the algorithm significantly dominate the ones generated by the current practice, in terms of fuel costs, while achieving similar path durations. The average number of non-dominated paths is 2.7, which allows decision makers to ultimately visually inspect the proposed alternatives.